Weekly Roadmap*

1. Getting started with R and RStudio
2. Plotting with {ggplot2}
3. Importing and transforming data
4. Exploratory data analysis
5. Interactive and animated plots
6. Mapping (will work in relational data)
7. Text mining
8. Flexdashboards (and shiny apps)
9. Tidymodels
10. Reproducible workflows

* subject to change

Today’s plan

• Show several methods for importing data
• Demonstrate common {dplyr} functions for wrangling data
• RMarkdown goal: output to Tufte handout

Today’s plot inspiration

Today’s plot goal

Importing data

• Local files
  • csv: read.csv()
  • RData: load()
  • Stata: haven::read_dta()
  • SPSS: haven::read_sav()
  • SAS: haven::read_sas()
• Web
  • csv: read.csv(INSERT URL HERE)
  • Google Sheets: googlesheets4::read_sheet()
  • APIs with {httr} and {jsonlite}
  • web scraping with {rvest}

Tidy data

Last week we talked about how tidy data facilitates plotting. According to Hadley Wickham, RStudio’s Chief Scientist and creator of many great packages like {ggplot2}, tidy data have three characteristics (Wickham & others, 2014):

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

dplyr

https://dplyr.tidyverse.org/

dplyr::mutate()

adds new variables that are functions of existing variables

dplyr::select()

picks variables based on their names

dplyr::filter()

picks cases based on their values

dplyr::summarize()

reduces multiple values down to a single summary

dplyr::arrange()

changes the ordering of the rows

dplyr::distinct()

keeps unique rows

RStudio